Distinct metabolic network states manifest in the gene expression profiles of pediatric inflammatory bowel disease patients and controls

Scientific Reports

Volumn 6, Issue , 2016, Pages

  Knecht, Carolin ^a   Fretter, Christoph ^b   Rosenstiel, Philip ^a   Krawczak, Michael ^a   Hütt, Marc Thorsten ^b  

^a UNIVERSITY OF KIEL   (Germany)

^b JACOBS UNIVERSITY BREMEN   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

ADOLESCENT; CASE CONTROL STUDY; CHILD; DATA MINING; FEMALE; GENE EXPRESSION PROFILING; GENE EXPRESSION REGULATION; GENETICS; HUMAN; INFLAMMATORY BOWEL DISEASE; INTESTINE; MALE; METABOLISM; PATHOLOGY; PHENOTYPE; PRESCHOOL CHILD;

ADOLESCENT; CASE-CONTROL STUDIES; CHILD; CHILD, PRESCHOOL; DATA MINING; FEMALE; GENE EXPRESSION PROFILING; GENE EXPRESSION REGULATION; HUMANS; INFLAMMATORY BOWEL DISEASES; INTESTINES; MALE; METABOLIC NETWORKS AND PATHWAYS; PHENOTYPE;

EID: 84986218244     PISSN: None     EISSN: 20452322     Source Type: Journal    
DOI: 10.1038/srep32584     Document Type: Article

References (36)

1. Barabási, A.-L., Gulbahce, N. & Loscalzo, J. Network medicine: a network-based approach to human disease. Nature Rev Genet 12, 56-68 (2011).
2. Barabási, A.-L. The network takeover. Nature Physics 8, 14-16 (2012).
3. Hütt, M.-T. Understanding genetic variation-the value of systems biology. Br J Clin Pharmacol 77, 597-605 (2014).
4. Ideker, T. & Krogan, N. J. Differential network biology. Molecular systems biology 8, 565 (2012).
5. Schreiber, S., Rosenstiel, P., Albrecht, M., Hampe, J. & Krawczak, M. Genetics of Crohn disease, an archetypal inflammatory barrier disease. Nature Rev Genet 6, 376-388 (2005).
6. Liu, J. Z. et al. Association analyses identify 38 susceptibility loci for inflammatory bowel disease and highlight shared genetic risk across populations. Nat Genet 47, 979-986, doi: 10. 1038/ng. 3359 (2015).
7. Fukushima, K., Yonezawa, H. & Fiocchi, C. Inflammatory bowel disease-associated gene expression in intestinal epithelial cells by differential cDNA screening and mRNA display. Inflamm Bowel Dis 9, 290-301 (2003).
8. Costello, C. M. et al. Dissection of the inflammatory bowel disease transcriptome using genome-wide cDNA microarrays. PLoS Med 2, e199, doi: 10. 1371/journal. pmed. 0020199 (2005).
9. Ben-Shachar, S. et al. Gene expression profiles of ileal inflammatory bowel disease correlate with disease phenotype and advance understanding of its immunopathogenesis. Inflamm Bowel Dis 19, 2509-2521, doi: 10. 1097/01. MIB. 0000437045. 26036. 00 (2013).
10. Holgersen, K. et al. High-resolution gene expression profiling using RNA sequencing in patients with inflammatory bowel disease and in mouse models of colitis. J Crohns Colitis 9, 492-506, doi: 10. 1093/ecco-jcc/jjv050 (2015).
11. Roediger, W. E. The colonic epithelium in ulcerative colitis: an energy-deficiency disease? Lancet 2, 712-715 (1980).
12. Thiele, I. et al. A community-driven global reconstruction of human metabolism. Nature Biotechnol 31, 419-425 (2013).
13. Haberman, Y. et al. Pediatric Crohn disease patients exhibit specific ileal transcriptome and microbiome signature. J Clin Invest 124, 3617 (2014).
14. Sonnenschein, N., Geertz, M., Muskhelishvili, G. & Hütt, M.-T. Analog regulation of metabolic demand. BMC Syst Biol 5, 40 (2011).
15. Sonnenschein, N. et al. A network perspective on metabolic inconsistency. BMC Syst Biol 6, 41 (2012).
16. Price, N. D., Reed, J. L. & Palsson, B. ∅. Genome-scale models of microbial cells: evaluating the consequences of constraints. Nature Rev Microbiol 2, 886-897 (2004).
17. Kauffman, K., Prakash, P. & Edwards, J. S. Advances in flux balance analysis. Curr Opin Biotechnol 14, 491-496 (2003).
18. Varma, A. & Palsson, B. ∅. Stoichiometric flux balance models quantitatively predict growth and metabolic by-product secretion in wild-type Escherichia coli W3110. AEM 60, 3724-3731 (1994).
19. Edwards, J. S., Ibarra, R. U. & Palsson, B. ∅. In silico predictions of Escherichia coli metabolic capabilities are consistent with experimental data. Nature Biotechnol 19, 125-130 (2001).
20. Famili, I., Forster, J., Nielsen, J. & Palsson, B. ∅. Saccharomyces cerevisiae phenotypes can be predicted by using constraint-based analysis of a genome-scale reconstructed metabolic network. PNAS 100, 13134-13139 (2003).
21. Duarte, N. C. et al. Global reconstruction of the human metabolic network based on genomic and bibliomic data. PNAS 104, 1777-1782 (2007).
22. Ma, H. et al. The Edinburgh human metabolic network reconstruction and its functional analysis. Molecular systems biology 3, 135 (2007).
23. Jerby, L., Shlomi, T. & Ruppin, E. Computational reconstruction of tissue-specific metabolic models: application to human liver metabolism. Molecular systems biology 6, 1-9 (2010).
24. Gille, C. et al. HepatoNet1: a comprehensive metabolic reconstruction of the human hepatocyte for the analysis of liver physiology. Molecular systems biology 6, 411, doi: 10. 1038/msb. 2010. 62 (2010).
25. Mintz-Oron, S. et al. Reconstruction of Arabidopsis metabolic network models accounting for subcellular compartmentalization and tissue-specificity. PNAS 109, 339-344 (2012).
26. Hedderich, J. & Sachs, L. Angewandte Statistik. Vol. 14 (Springer-Verlag, 2012).
27. Hampel, F. R. The Breakdown Points of the Mean Combined with Some Rejection Rules. Technometrics 27, 95-107, doi: 10. 2307/1268758 (1985).
28. Heimerl, S. et al. Alterations in intestinal fatty acid metabolism in inflammatory bowel disease. Biochim Biophys Acta 1762, 341-350, doi: 10. 1016/j. bbadis. 2005. 12. 006 (2006).
29. Rachmilewitz, D. & Saunders, D. R. Metabolism of chenodeoxycholate by intestinal mucosa. Gastroenterology 71, 82-86 (1976).
30. Matern, S., Matern, H., Farthmann, E. H. & Gerok, W. Hepatic and extrahepatic glucuronidation of bile acids in man. Characterization of bile acid uridine 5?-diphosphate-glucuronosyltransferase in hepatic, renal, intestinal microsomes. The Journal of clinical investigation 74, 402-410, doi: 10. 1172/JCI111435 (1984).
31. Müller, N. et al. IL-6 blockade by monoclonal antibodies inhibits apolipoprotein (a) expression and lipoprotein (a) synthesis in humans. J Lipid Res 56, 1034-1042, doi: 10. 1194/jlr. P052209 (2015).
32. Subramanian, V. S., Nabokina, S. M. & Said, H. M. Association of TM4SF4 with the human thiamine transporter-2 in intestinal epithelial cells. Dig Dis Sci 59, 583-590, doi: 10. 1007/s10620-013-2952-y (2014).
33. Joshi, A. D. et al. Four Susceptibility Loci for Gallstone Disease Identified in a Meta-analysis of Genome-Wide Association Studies. Gastroenterology, doi: 10. 1053/j. gastro. 2016. 04. 007 (2016).
34. Vermeire, S., Van Assche, G. & Rutgeerts, P. Role of genetics in prediction of disease course and response to therapy. World J Gastroenterol 16, 2609-2615 (2010).
35. Dubinsky, M. Special issues in pediatric inflammatory bowel disease. World J Gastroenterol 14, 413-420 (2008).
36. R Development Core Team. R: A Language and Environment for Statistical Computing. (R Foundation for Statistical Computing, 2016).